Summarization of football video content

ABSTRACT

Summarization of video content including football.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional application of and claimspriority to U.S. patent application Ser. No. 09/933,862, filed Aug. 20,2001.

BACKGROUND OF THE INVENTION

The present invention relates to summarization of video contentincluding football.

The amount of video content is expanding at an ever increasing rate,some of which includes sporting events. Simultaneously, the availabletime for viewers to consume or otherwise view all of the desirable videocontent is decreasing. With the increased amount of video contentcoupled with the decreasing time available to view the video content, itbecomes increasingly problematic for viewers to view all of thepotentially desirable content in its entirety. Accordingly, viewers areincreasingly selective regarding the video content that they select toview. To accommodate viewer demands, techniques have been developed toprovide a summarization of the video representative in some manner ofthe entire video. Video summarization likewise facilitates additionalfeatures including browsing, filtering, indexing, retrieval, etc. Thetypical purpose for creating a video summarization is to obtain acompact representation of the original video for subsequent viewing.

There are two major approaches to video summarization. The firstapproach for video summarization is key frame detection. Key framedetection includes mechanisms that process low level characteristics ofthe video, such as its color distribution, to determine those particularisolated frames that are most representative of particular portions ofthe video. For example, a key frame summarization of a video may containonly a few isolated key frames which potentially highlight the mostimportant events in the video. Thus some limited information about thevideo can be inferred from the selection of key frames. Key frametechniques are especially suitable for indexing video content but arenot especially suitable for summarizing sporting content.

The second approach for video summarization is directed at detectingevents that are important for the particular video content. Suchtechniques normally include a definition and model of anticipated eventsof particular importance for a particular type of content. The videosummarization may consist of many video segments, each of which is acontinuous portion in the original video, allowing some detailedinformation from the video to be viewed by the user in a time effectivemanner. Such techniques are especially suitable for the efficientconsumption of the content of a video by browsing only its summary. Suchapproaches facilitate what is sometimes referred to as “semanticsummaries.”

What is desired, therefore, is a video summarization technique suitablefor video content that includes football.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary flowchart for play detection.

FIG. 2 is an exemplary illustration of a hiking scene in football.

FIG. 3 is an exemplary illustration of a kicking scene in football.

FIG. 4 illustrates one example of a generally green color region.

FIG. 5 is a technique for defining the generally green color region.

FIG. 6 is a technique for defining histograms for the field frames.

FIG. 7 illustrates the definition of a central region of a frame and/orfield.

FIG. 8 illustrates candidate frame selection based upon an initialgenerally green selection.

FIG. 9 is an exemplary illustration of a hiking scene in football.

FIG. 10 illustrates edge detection for the image in FIG. 9.

FIG. 11 illustrates parametric lines for the edge detection of FIG. 10.

FIG. 12 illustrates computed motion vectors for football video.

FIG. 13 illustrates an exemplary start of a football play.

FIG. 14 illustrates a green mask for the image of FIG. 13.

FIG. 15 illustrates an exemplary green mask for an image of a footballplayer.

FIG. 16 illustrates an exemplary football player.

FIG. 17 illustrates a projection of the green mask of FIG. 14.

FIG. 18 illustrates a projection of the green mask of FIG. 15.

FIG. 19 is an illustration of temporal evidence accumulation.

FIG. 20 is an illustration of the U-V plane.

FIG. 21 is an illustration of detecting the end of a play in football.

FIG. 22 illustrates the preferred technique to identify the end of theplay.

FIG. 23 illustrates the detection of black frames for commercials.

FIG. 24 illustrates an exemplary technique for segment removal basedupon commercial information.

FIGS. 25A-25C illustrate audio segments of different plays.

FIG. 26 illustrates forming a multi-layered summary of the originalvideo sequence.

FIG. 27 illustrates the video summarization module as part of a mediabrowser and/or a service application.

FIG. 28 illustrates a video processing system.

FIG. 29 illustrates an exemplary overall structure of the footballsummarization system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A typical football game lasts about 3 hours of which only about one hourturns out to include time during which the ball is in action. The timeduring which the ball is in action is normally the exciting part of thegame, such as for example, a kickoff, a hike, a pass play, a runningplay, a punt return, a punt, a field goal, etc. The remaining timeduring the football game is typically not exciting to watch on video,such as for example, nearly endless commercials, the time during whichthe players change from offense to defense, the time during which theplayers walk onto the field, the time during which the players are inthe huddle, the time during which the coach talks to the quarterback,the time during which the yardsticks are moved, the time during whichthe ball is moved to the spot, the time during which the spectators areviewed in the bleachers, the time during which the commentators talk,etc. While it may indeed be entertaining to sit in a stadium for threehours for a one hour football game, many people who watch a video of afootball game find it difficult to watch all of the game, even if theyare loyal fans. A video summarization of the football video, whichprovides a summary of the game having a duration shorter than theoriginal football video, may be appealing to many people. The videosummarization should provide nearly the same level of the excitement(e.g. interest) that the original game provided.

Upon initial consideration, football would not be a suitable candidateto attempt automated video summarization. Initially, there are nearly anendless number of potential plays that may occur which would need to beaccounted for in some manner. Also, there are many different types ofplays, such as a kickoff, a punt, a pass play, a kickoff return, arunning play, a reverse play, an interception, a sack, etc., thatlikewise would need to be accounted for in some manner. In addition,each of these plays involves significant player motion which isdifficult to anticipate, difficult to track, and is not consistentbetween plays. Moreover, the ball would normally be difficult, if notimpossible, to track during a play because much of the time it isobscured from view. For example, it would be difficult to distinguishinteresting play related activity from typical pre-play activity of theplayers walking around the field getting ready for the next play. Basedupon such considerations has been previously considered impractical, ifnot impossible, to attempt to summarize football.

It is conceivably possible to develop highly sophisticated models of atypical football video to identify potentially relevant portions of thevideo. However, such highly sophisticated models are difficult to createand are not normally robust. Further, the likelihood that a majority ofthe highly relevant portions of the football video will be included insuch a video summarization is low because of the selectivity of themodel. Thus the resulting video summarization of the football game maysimply be unsatisfactory to the average viewer.

After consideration of the difficulty of developing highly sophisticatedmodels of a football video to analyze the content of the football video,as the sole basis upon which to create a football summarization, thepresent inventors determined that this technique is ultimately flawed asthe models will likely never be sufficiently robust to detect all thedesirable content. Moreover, the number of different types of modelsequences of potentially desirable content is difficult to quantify. Incontrast to attempting to detect particular model sequences, the presentinventors determined that the desirable segments of the football gameare preferably selected based upon a “play.” A “play” may be defined asan sequence of events defined by the rules of football. In particular,the sequence of events of a “play” may be defined as the time generallyat which the ball is put into play (e.g., a time based upon when theball is put into play) and the time generally at which when the ball isconsidered out of play (e.g., a time based upon when the ball isconsidered out of play). Normally the “play” would include a relatedseries of activities that could potentially result in a score (or arelated series of activities that could prevent a score) and/orotherwise advancing the team toward scoring (or prevent advancing theteam toward scoring).

An example of an activity that could potentially result in a score, mayinclude for example, throwing the ball far down field, kicking a fieldgoal, kicking a point after, and running the ball. An example of anactivity that could potentially result in preventing a score, mayinclude for example, intercepting the ball, recovering a fumble, causinga fumble, dropping the ball, and blocking a field goal, punt, or pointafter attempt. An example of an activity that could potentially advancea team toward scoring, may be for example, tackling the runner running,catching the ball, and an on-side kick. An example of an activity thatcould potentially prevent advancement a team toward scoring, may be forexample, tackling the runner, tackling the receiver, and a violation. Itis to be understood that the temporal bounds of a particular type of“play” does not necessarily start or end at a particular instance, butrather at a time generally coincident with the start and end of the playor otherwise based upon, at least in part, a time (e.g., event) basedupon a play. For example, a “play” starting with the hiking the ball mayinclude the time at which the center hikes the ball, the time at whichthe quarterback receives the ball, the time at which the ball is in theair, the time at which the ball is spotted, the time the kicker kicksthe ball, and/or the time at which the center touches the ball prior tohiking the ball. A summarization of the video is created by including aplurality of video segments, where the summarization includes fewerframes than the original video from which the summarization was created.A summarization that includes a plurality of the plays of the footballgame provides the viewer with a shorted video sequence while permittingthe viewer to still enjoy the game because most of the exciting portionsof the video are provided, preferably in the same temporally sequentialmanner as in the original football video.

Referring to FIG. 1, a procedure for summarization of a football videoincludes receiving a video sequence 20 that includes at least a portionof a football game. Block 22 detects the start of a play of a videosegment of a plurality of frames of the video. After detecting the startof the play, block 24 detects the end of the play, thereby defining asegment of video between the start of the play and the end of the play,namely, a “play”. Block 26 then checks to see if the end of the video(or the portion to be processed) has been reached. If the end of thevideo has not been reached block 26 branches to block 22 to detect thenext play. Alternatively, if the end of the video has been reached thenblock 26 branches to the summary description 28. The summary descriptiondefines those portions of the video sequence 20 that contain therelevant segments for the video summarization. The summary descriptionmay be compliant with the MPEG-7 Summary Description Scheme orTV-Anytime Segmentation Description Scheme. A compliant media browser,such as shown in FIG. 27, may apply the summary description to the inputvideo to provide summarized viewing of the input video without modifyingit. Alternatively, the summary description may be used to edit the inputvideo and create a separate video sequence. The summarized videosequence may comprise the selected segments which excludes at least aportion of the original video other than the plurality of segments.Preferably, the summarized video sequence excludes all portions of theoriginal video other than the plurality of segments.

The present inventors then considered how to detect a “play” from afootball video in a robust, efficient, and computationally effectivemanner. After extensive analysis of a typical football game it wasdetermined that a football game is usually captured by cameraspositioned at fixed locations around the football field, with eachcamera typically capable of panning, tilting, and zooming. Each play ina football game normally starts with the center hiking the ball, such astoward the quarterback or kicker. Further, a hiking scene, in which thecenter is about to hike the ball, is usually captured from a cameralocation to the side of the center. This camera angle is typically usedbecause it is easier to observe the movements of all of the partiesinvolved (the offense, the center, the quarterback, the receivers, therunning back, and the defense) from this viewpoint. Thus a playtypically starts with a frame such as shown in FIG. 2.

While an attempt to determine a hiking scene may include complexcomputationally intensive analysis of the frame(s) to detect the center,the quarterback, or the kicker, and the offense/defense, together withappropriate motion, this generally results in non-robust hiking scenedetection. To overcome this limitation the present inventors weredumbfounded to recognize that the scenes used to capture a footballvideo typically use the same set of camera angles. The football gamenormally includes cameras sitting either on one side of the field and onthe two ends of the field. The side cameras are normally located in thestadium above the 25, 50, and 25 yard lines, and the two end cameras arelocated at the ends of the fields. There may be additional cameras, suchas handheld cameras, but most of the events are captured by the sidecameras and the end cameras. In general there are two different types ofplays, namely, place kicks and regular plays (e.g., plays that are notplace kicks). In general, place kicks (which include the kick-offs,extra point attempts, and field goal attempts) are usually captured by acamera near the end of the field, while a regular play (including runs,passes, and punts) is usually captured by a side camera. It is alsonoted that a kick-off is usually captured by an end camera followed by aside camera. Accordingly, the different plays of a football video may becategorized as one of two different types of plays, namely, a placekick, and a regular play.

The regular play typically starts with a frame such as that shown inFIG. 2. The camera then follows the ball until the ball is called dead,at which time the current regular play ends. After the end of theregular play there is typically a camera break, at which time the cameraviews other activity, such as the commentators or the fans. The timebetween the camera break and the start of the next play is usually notexciting and thus should not be included in the summary.

The place kick typically starts with a frame such as that shown in FIG.3, and it normally ends with a camera break, in a manner similar to theregular play. For the place kick, there are normally more than onecamera break before the end of the play, such as for example, a firstcamera break at the switch from the end camera to the side camera, and asecond camera break when the play ends.

To determine a start of a play, such as those shown in FIGS. 2 and 3,the present inventors considered criteria that may be suitable tocharacterize such an event. The criteria to determine the start of theplay is based on anticipated characteristics of the image, as opposed toanalyzing the content of the video to determine the actual events. Onecriteria that may be used to determine the start of a play is the fieldcolor. Under the assumption that a camera provides a typical start framelike those shown in FIG. 2 or 3, it may be observed that the field has agenerally green color. Accordingly, a characteristic of the start of aplay may be if a sufficient spatial region of the frame has thegenerally green color. The sufficient spatial generally green region maybe further defined by having shape characteristics, such assubstantially straight edges, a set of substantially parallel edges, afour-sided polygon, etc. Further, the spatial region of the generallygreen color is preferably centrally located within the frame. Thus, itwould initially appear that the start of a play can be detected bylocating frames with a generally green dominant color in the centralregion. The aforementioned color test is useful in detecting the startof a play. However, after further analysis it was determined that merelydetecting the generally green dominant color centrally located issufficient but may be insufficient for a robust system. For example insome implementations, a dominant generally green color may be anecessary condition but not a sufficient condition for determining thestart frame of play.

For example, the color characteristic of a central spatial generallygreen region may exist when the camera is focused on a single player onthe field prior to a play. In addition, the precise color of thegenerally green color captured by the camera varies from field to field,from camera to camera, and from day to night. In fact, even for a givengame, since it may start in late afternoon and last into early evening,the lighting condition may change, causing the generally green color ofthe same field to vary significantly during the video. Moreover, thegenerally green field color is typically not uniform and includesvariations. Thus it is preferably not to use a narrow definition of thegenerally green color (e.g., excluding other non-green specific colors).Therefore, it is preferable to use a broad definition of generallygreen. If a broad definition of a generally green color is used, such asones that includes portions of other colors, then a greater number ofnon-play scenes will be identified.

With the generally green color of the field not being constant, it isdesirable to calibrate the generally green color for a specific footballvideo. Further, it is desirable to calibrate the generally green colorfor a specific portion of a football video, with the generally greencolor being recalibrated for different portions of the football video.Referring to FIG. 4, using the hue component in the HSV color space asan example, the preferred system provides a range of generally greencolors, such as G_(low) and G_(high), with generally green being definedthere between. The G_(low) and/or G_(high) may be automatically modifiedby the system to adapt to each particular football video and todifferent portions of the video.

With the variation of the field color even within a game, the presentinventors determined that a color histogram H_(g) of the generally greencolor in addition to a range given by G_(low) and G_(high), provides amore accurate specification of the field color. The H_(g) may calibratedfor a specific football video. Also H_(g) may be calibrated for aspecific portion of the football video, with the H_(g) beingrecalibrated for different portions of the football video. Even with twoframes of the video showing the field the resulting color histogramswill tend to be different. Thus, it is useful to estimate the extent towhich the field color histograms vary in a particular football video, orportion thereof. It is preferable to use the field scenes, howeverdetected, from which to estimate the color histograms.

The following technique may be used to determine G_(low), G_(high), andH_(g). Referring to FIG. 5, for all (or a portion of) the framescontaining the field all the generally green pixels are located. Forthis initial determination preferably the generally green pixels aredefined to include a large interval. The interval may be defined asG0=[G0 _(low), G0 _(high)]. Next a statistic measure of the generallygreen pixels is calculated, such as the mean hue green value G_(mean) ofall the pixels. Next G_(low) and G_(high) may be set. One technique forsetting G_(low) and G_(high) is: G_(low)=G_(mean)−g, G_(high =G)_(means) +g, where g is a constant such that G_(high)−G_(low)<G0_(high)−G0 _(low). In essence, the technique narrows (i.e., reduces itsgamut) the range of generally green colors based on color basedinformation from the football video.

The following technique may be used to determine the color histogramH_(g). Referring to FIG. 6, all (or a portion of) the frames containingthe field are selected. Within these field frames all (or a portion on)the pixels falling in the range of G_(low) and G_(high) are selected.Other ranges of generally green colors may likewise be used. The colorhistogram H_(i) for each of these sets of pixels in each of the framesis then determined. Then H_(g) is computed as a statistical measure,such as the average, of all the calculated color histograms H_(i). Inparticular the variation of H_(g) maybe calculated as follows.

For any frame containing the field, compute the error between H_(i) andH_(g):e _(i) =∥H _(g) −H _(i)∥ where ∥•∥ is the L₁ norm.

The sample mean is computed as:

$m_{e} = {\frac{1}{N}{\sum\limits_{i}e_{i}}}$

The sample standard deviation of all the errors is calculated as:

$v = {\sum\limits_{i}\left( {\left( {e_{i} - m_{e}} \right)^{2}/\left( {N - 1} \right)} \right)^{1/2}}$

with N being the number of frames, v being a measure for evaluating howa color histogram is different from the average H_(g).

With the green color being calibrated, the system may test if a frame islikely the start of a play by checking the following two conditions:

-   -   (1) if the frame has more than P₁% generally green pixels;    -   (2) if the color histogram H₁ of these generally green pixels is        close enough to Hg.

The first condition may be examined by counting the number of pixelswhose hue value falls in G_(low), G_(high). The second condition may beexamined by checking if the difference between H₁ and H_(g) is smallerthan a threshold, i.e., if ∥H₁−H_(g)∥<T_(h). The threshold T_(h) may bedetermined as:T _(h) =m _(e) +c•v,

where c is a constant, typically 3 or 4.

If both conditions are satisfied, then a potential start is detected,and this frame may then be further checked by other modules if it isdesirable to confirm a detection. If however, the frame has only morethan P₂% green pixels (P₂<P₁), and the second condition is satisfied,then the field line detection module described later should be used toincrease the confidence of an accurate determination of a potentialstart of a play.

After consideration of actual frames of the start of a play in footballvideos the present inventors observed that sometimes the start framescontain non-field regions on the top and the bottom, and further maycontain editing bars on the side or on the bottom. These factors are notespecially compatible with the use of the thresholds P₁ and P₂, aspreviously described. For the thresholds P₁ and P₂ to be more robust,only the center region (e.g., primarily generally within such non-fieldregions and editing bars) of the frame should be used when computing thepercentages. Referring to FIG. 7, the center region may be defined asfollows.

-   -   (1) scan a frame row-by row, starting from the first row, until        a row that has dominant generally green pixels is located, or        until a predetermined maximum is reached, whichever occurs        first;    -   (2) scan the frame row-by-row, starting from the bottom row,        until a row that has dominant generally green pixels is located,        or until a predetermined maximum is reached, whichever occurs        first;    -   (3) scan the frame column-by-column, starting from the right        column until a column that has dominant generally green pixels        is located, or until a predetermined maximum is reached,        whichever occurs first;    -   (4) scan the frame column-by-column, starting from the left        column until a column that has dominant generally green pixels        is located, or until a predetermined maximum is reached,        whichever occurs first;    -   (5) the locations at which the scanning stopped (e.g., found the        dominant generally green color or otherwise a predetermined        maximum), defines the central region of the frame.

The preferred predetermined maximums are ¼ of the row number as theconstant in the scanning of the rows and ⅙ of the column number as theconstant in the scanning of the columns.

After further consideration of the football video, the present inventorslikewise observed a pattern exhibited by the football video at the startof a play, namely, the field lines. The presence of the field lines is astrong indication of the existence of a corresponding field being viewedby the camera. The field lines maybe characterized by multiplesubstantially parallel spaced apart substantially straight lines orlines on a contrasting background. The field lines may alternatively becharacterized by multiple spaced apart generally white lines. Inaddition, the field lines may be characterized as a pattern of lines ona background primarily a generally green color. Also, the field linesmay be further constrained as being of a sufficient length relative tothe size of the field or image. In the preferred system, the field linesare characterized as two, three, or four of the above. This lengthconsideration removes shorter lines from erroneously indicating a field.The identification of the frames of video representing fields using thefield lines may be used as the basis for the color calibration, ifdesired.

Referring to FIG. 8, the preferred system includes candidate frameselection by using an initial green specification, such as G0=[G0_(low), G0 _(high)]. Then those frames with a primary color G0 areidentified. A green mask may be obtained by setting a value of “1” tolocations defined by the G0 color and “0” to the other locations. Thegreen mask may then be diluted, if desired, to allow the inclusion ofsmall regions adjacent to the green G0 region. The edge detection maythen be performed on the frames followed by filtering with the greenmask. This step is intended to eliminate those edge pixels that are noton the generally green background. A line detection is then performed onthe filtered edge map, such as with a Hough transform, to get lines thatare longer than L_(min). It is to be understood that any suitabletechnique may be used to identify the lines, and in particular the lineswithin a generally green background.

After experimentation with the line detection scheme there remains asmall probability that such line detection will result in falsepositives, even in a generally green background. The present inventorsfurther considered that an image of a field from a single viewpointresults in some distortion of the parallel alignment of the field lines.In particular, a plurality of the field lines will appear to converge atsome point (or points). Preferably, all of the field lines will appearto pass through approximately the same disappearing point since thefield lines are parallel to one another on the field. Referring to FIG.9, a sample frame is shown. Referring to FIG. 10, the result of the edgedetection is shown. Referring to FIG. 11, the parametric lines along thevertical direction are illustrated, with the lines passing generallythrough the same point.

In the preferred system, the condition that is used is detecting atleast three lines that pass through approximately the same point whenprojected. This additional condition, especially when used inconjunction with previous field line determination, significantlydecreases the likelihood of false positives. Similarly, when the frameis from an end camera, such as shown in FIG. 3, the field lines wouldappear to be nearly horizontal and parallel to each other in the imagedomain, which is likewise a test for determination of a field. As shownin FIG. 8, in either case (side view of the field or end view of thefield) the task is to test if the lines are parallel in the physicalworld, and this is referred to as the parallelism test. After theparallelism test the green may be calibrated and the start of a play maybe determined based upon these characteristics.

The present inventors observed that there are some cases where the fieldmay contain multiple regions of clay which is of generally brown color.The color calibration technique described above can be similarly appliedto deal with these cases so that the system can handle fields ofgenerally green color, fields of generally green and generally browncolors, and fields of generally brown color. Other techniques maylikewise be applied to the generally brown, or generally brown andgenerally green.

The present inventors observed that in many cases the two teams arelined up and most of the motion stops before the start of a play. Atthis point, the camera motion may tend to zoom in to get an improvedpicture and stays focused on the players until the play starts. Thus atthe moment right before a play starts, there will tend to be nosignificant motion in the image domain (neither camera-induced motionnor player motion). Therefore, the present inventors determined that thecamera motion may be used as an additional indicia of the start of aplay. In many instances, a start-of-play will induce a zooming in cameramotion that then stops zooming with the scene being free fromsignificant motion. This is another characteristic that may be used toindicate the start of plays. This technique may likewise be used inconjunction with other techniques to decrease false positives.

There are several techniques that may be used for estimating cameramotion. Some methods such as optical flow estimation may provide densemotion fields and hence provide relatively accurate motion estimationresults. However, optical flow techniques and similar techniques, arecomputationally expensive. A less computationally expensive technique isto infer the camera motion from block-based motion compensation. Inaddition, the motion information is available without additionalcomputation if the system is operating on compressed streams of encodedvideo, such as a MPEG-like bitstream. It has been determined that thetranslational motion can be accurately estimated from the motion vectorswhereas zooming is not accurately estimated from the motion vectors. Theinaccuracy of the motion vectors for zooming may be based on the varyingrate of zooming and the scale changes induced by zooming. Therefore, themotion information is preferably used in the following manner: if thecamera motion is not primarily translational, the system waitsadditional frames to confirm the start of a play; otherwise, thestart-of-play is declared as long as other conditions are satisfied. Awaiting period in the first has dual functions: firstly, it excludesfrom the summary some frames when the camera is zooming before a startof the play; and secondly, it makes the detection of the start-of-playmore robust since more frames have been used to confirm the detection.FIG. 12 illustrates an example of computed motion vectors, when thecamera is switched on after a play has started. It is not difficult todeduce that the camera is panning in this situation, based on theprimary direction of the motion vectors. In this case a start-of-playmay be declared.

As illustrated in FIGS. 2 and 3, in a start-of-play frame, the playersappear as scattered blobs in the image. The blobs may be represented bytheir color and/or texture, and compared against a model of theanticipated color and/or texture for a player. The color and/or texturemay be varied, based on the particular team's clothing. In this manner,the system is customizable for particular teams. In the case that thereare scattered non-generally green blobs their color characteristics maybe compared against a model. In addition, the system may determine,using other techniques, to determine potential start of play frames anduse these frames as the basis to calculate color histograms for theplayers.

Referring to FIG. 13, at the start of the football play the each of theteams tend to line up in some manner. This line up of the players may beused as a characteristic upon which to determine the start of a play.The characteristic of a suitable line up of players includes a generallyaligned set of non-generally green blobs (e.g., regions), such as thegreen mask shown in FIG. 14, as previously described. Further, the blobsshould have a relatively small size, especially in relation to the sizeof the field. In contrast, a relatively large non-generally green blob,such as the green mask shown in FIG. 15, is more likely indicative of aclose up of a player, such as shown in FIG. 16. To characterize thespatial distribution of the non-generally green regions the green masksmay be projected into x and y directions, such as shown in FIG. 17 andFIG. 18. A high and wide peak in the projection, as shown in FIG. 18, isless likely to indicate the start of a play than a generally low set ofpeaks, as shown in FIG. 17. Another approach for analyzing the line upof players may be determining two distinctive groups of blobs lining upalong both sides of a “line” that is parallel to the field lines.

After further consideration, the present inventors determined that if ahiking scene and accordingly a play segment is identified after locatingonly one candidate frame, then the system may be susceptible to falsepositives. By examining a set of consecutive frames (or other temporallyrelated frames) and accumulating evidence, the system can reduce thefalse positive rate. Referring to FIG. 19, the following approach may beused to achieve temporal evidence of accumulation: when detecting ahiking scene, a sliding window of width w is used (e.g., w frames areconsidered at the same time). A hiking scene is declared only if morethan p out of the w frames in the current window are determined to behiking scene candidates, as previously described. A suitable value of pis such that p/w=70%. Other statistical measures may be used of a fixednumber of frames or dynamic number of frames to more accuratelydetermine hiking scenes.

To define the “generally green” color any color space may be used. Thepreferred color space is the HSV color space because it may be usedwithout excessive computational complexity. Alternatively, a YUV colorspace may be used as shown in FIG. 20.

While the start of a “play” may be defined as a hiking scene the end ofa play, according to the rules of football, can end in a variety ofdifferent ways. Image analysis techniques may be used to analyze theimage content of the frames after a hiking frame to attempt to determinewhat occurred. Unfortunately, with the nearly endless possibilities andthe difficultly of interpreting the content of the frames, thistechnique is at least, extremely difficult and computationallyintensive. In contrast to attempting to analyze the content of thesubsequent frames of a potential play, the present inventors determinedthat a more efficient manner for the determination of the extent of aplay in football is to base the end of the play on camera activities.After analysis of a football video the present inventors were surprisedto determine that the approximate end of a play may be modeled by scenechanges, normally as a result of switching to a different camera or adifferent camera angle. The different camera or different camera anglemay be modeled by determining the amount of change between the currentframe (or set of frames) to the next frame (or set of frames).

Referring to FIG. 21, a model of the amount of change between framesusing a color histogram difference technique for an exemplary 1,000frame video football clip is shown. The peaks typically correspond toscene cuts. The system may detect an end of play at around frame 649 bythresholding the color histogram difference. A gradual transition occursaround frame 350.

As previously noted the scene cuts may be detected by thresholding thecolor histogram differences. The selection of the an appropriatethreshold level to determine scene cuts may be based on a fixedthreshold, if desired. The appropriate threshold level may be calculatedfor each football video, either after processing a segment of the videoor otherwise dynamically while processing the video. One measure of thethreshold level may be based upon the mean m and the standard deviationσ of the frame-to-frame color histogram differences from the wholevideo. The threshold Tc can be calculated as m+cσ where c is a constant.It has been found that c=5 or 6 covers practically almost all the cleanscene cuts. For robustness, after a clean cut has been detected at framek, the system may further compute the color histogram difference betweenframe k−1 and k+1. This difference should be at least comparable to thatbetween k−1 and k. Other comparisons may likewise be used to determineif the difference is a false positive. Otherwise the cut at k may be afalse positive. This concept may be generalized to testing the colorhistogram difference between k−c and k+c, with c being a small positiveinteger (number of frames).

Even with the aforementioned technique there may be some falsedetections which do not correspond to a real play. Also, there aresituations in which a play is broken into two segments due to forexample, dramatic lighting fluctuations (mistaken by the system as ascene cut). Some of these problems can be remedied by post-processing.One example of a suitable post processing technique is if two plays areonly separated by a sufficiently short time duration, such as less thana predetermined time period, then they should be connected as a singleplay. The time period between the two detected plays may be includedwithin the total play, if desired. Even if the two detected plays areseparated by a short time period and the system puts the two playstogether, and they are in fact two separate plays, this results in anacceptable segment (or two plays) because it avoids frequent audio andvisual disruptions in the summary, which may be objectionable to someviewers. Another example of a suitable post processing technique is thatif a play has a sufficiently short duration, such as less than 3seconds, then the system should remove it from being a play because itis likely a false positive. Also, post-processing may be applied tosmoothen the connection between adjacent plays, for both video andaudio.

When the system is used in an “on-line” environment the entire video isnot available for processing. When used in an on-line environment thethreshold Tc may be computed based upon m and σ for the currentlyavailable (or a portion thereof) frames. In addition, to reducecomputational complexity, the frames in a single play may be used uponwhich to calculate m and σ.

Football video tends to include gradual transitions between plays andother activities, such as commentary. These gradual transitions tend tobe computationally complex to detect in the general case. However, inthe case of football it has been determined that detecting gradualtransitions based upon the color histogram differences is especiallysuitable. Other techniques may likewise be used. Referring to FIG. 22,the preferred technique may include starting from a start-of-play time(t_(o)) and looking forward until a sufficiently large scene change isdetected or until time t_(o)+t_(p) is reached, whichever occurs first.T_(p) relates to the maximum anticipated play duration and thereforeautomatically sets a maximum duration to the play. This time period forprocessing to locate gradual transitions is denoted as t_(clean) _(—)_(cut). If t_(clean) _(—) _(cut)<t_(low) then the system will notgradual scene cut and set the previously detected scene cut as the endof the play. This corresponds to an anticipated minimum time durationfor a play and t_(low) is used to denote the minimum time period.Otherwise, the system looks for the highest color histogram differencein the region t_(low), t_(clean) _(—) _(cut) or other measure of apotential scene change. This region of the segment is from the minimumtime duration to the next previously identified scene cut. Thisidentifies the highest color histogram difference in the time durationwhich may be a potential scene change. The time of the highest colorhistogram difference is identified at t₁. In a neighborhood of t₁,[t₁−c₁, t₂+c₂], a statistical computation is performed, such ascomputing the mean m₁ and the standard deviation σ of the colorhistogram differences. C₁ and c₂ are constants or statisticallycalculated temporal values for the region to examine around the highestcolor histogram difference. A mean filtering emphasizes regions having arelatively large difference in a relatively short time interval. If thecolor histogram differences at t₁ exceeds m₁+c₃*σ₁, where c₃ is aconstant (or otherwise) and some of its neighbors (or otherwise) aresufficiently large, then the system considers a gradual transition tohave occurred at around time (frame) t₁. The play is set to the shorterof the previously identified scene cut or the gradual transition, ifany.

Besides the technique of using field lines to assist in calibrating thefield colors, there are other techniques of color calibration. Forexample, the calibration may be performed by a human operator or by thesystem with the assistance of a human operator. The system may performautomatic calibration by using appropriate statistical techniques. Asimple technique is as follows. If the system has obtained a set ofhiking scene candidates, the system can estimate the color histogramsfor green colors from these candidates. Under the assumption that mostof the candidates are true hiking scene frames, the system can detectstatistical outliers in this set. The system then uses the remainingcandidate frames to estimate the specifics of the colors. With the greencolors calibrated the system can perform both the start-of-playdetection and the end-of-play detection more accurately.

A commercial detection module may be used to further refine thepotential play segments by removing those segments that are containedwithin commercials. In the broadcast industry, one or more black framesare inserted into the program to separate different commercials in thesame commercial session. Referring to FIG. 23, an example of thedistribution of black frames in a video of 35,000 frames, where a lineshows the location of a black frame. Visually, it becomes apparent thatthe clusters of black frames are commercials. One technique for thedetection of clusters, is shown in FIG. 24. The algorithm presumes thata regular program session will last at least Tm minutes. For example, Tmis typically larger than three minutes. On the other hand, it isunlikely that a single commercial will last more than two minutes. Thus,as long as black frames are used to separate different commercials in acommercial session, the preferred system will operate properly. Bysetting Tm reasonably large (e.g., three minutes), the system can stilldetect commercials even if not all the commercials in a commercialsession are separated by black frames. Also, a reasonably large Tm willreduce the likelihood that the regular program is mis-classified as acommercial.

If desired, a slow motion replay detection module may be incorporated.The system detects if a slow motion replay has occurred, which normallyrelates to important events. The system will capture the replays ofplays, the same as the typical non-slow motion replay (full speed), ifthe same type of camera angles are used. The play segments detected maybe identified with multiple characteristics, namely, slow motionreplay-only segments, play only segments without slow motion replaysegments, and slow motion replay that include associated full speedsegments. The resulting summary may include one or more of the differentselections of the aforementioned options, as desired. For example, theresulting summary may have the slow-motion replays removed. Theseoptions may likewise be user selectable.

While an effective summarization of a football video may be based on theconcept of the “play,” sometimes the viewer may prefer an even shortersummarization with the most exciting plays included. One potentialtechnique for the estimation of the excitement of a play is to performstatistical analysis on the segments to determine which durations aremost likely to have the highest excitement. However, this technique willlikely not provide sufficiently accurate results. Further, excitementtends to be a subjective measure that is hard to quantify. After furtherconsideration the present inventors came to the realization that theaudio provided together with the video provides a good indication of theexcitement of the plays. For example, the volume of the response of theaudience and/or the commentators provides a good indication of theexcitement. The louder audience and/or commentator acclaims the greaterthe degree of excitement.

Referring to FIGS. 25A-25C, an exemplary illustration is shown of audiosignals having a relatively quiet response (FIG. 25A), having a strongresponse (FIG. 25B), and having an extremely strong response (FIG. 25C).In general, it has been determined that more exciting plays have thefollowing audio features. First, the mean audio volume of the play islarge. The mean audio volume may be computed by defining the mean volumeof a play as

$v = {\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}{S^{2}(i)}}}$where S(i) is the i-th sample, and the N is the total number of samplesin the play. Second, the play contains more audio samples that havemiddle-ranged magnitudes. The second feature may be reflected by thepercentage of the middle-range-magnitude samples in the play, which maybe computed as

$P = {\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}{I\left( {{{s(i)}} > {{t1}\mspace{14mu}{and}\mspace{14mu}{{s(i)}}} < {t2}} \right)}}}$with I( ) being the indicator function (I(true)=1, and I(false)=0), t1and t2 are two thresholds defining the middle range.

Referring to FIG. 26, the first layer of the summary is constructedusing the play detection technique. The second and third layers (andother) are extracted as being of increasingly greater excitement, basedat least in part, on the audio levels of the respective audio of thevideo segments. Also, it would be noted that the preferred audiotechnique only uses the temporal domain, which results in acomputationally efficient technique. In addition, the level of the audiomay be used as a basis for the modification of the duration of aparticular play segment. For example, if a particular play segment has ahigh audio level then the boundaries of the play segment may beextended. This permits a greater emphasis to be placed on those segmentsmore likely to be exciting. For example, if a particular play segmenthas a low audio level then the boundaries of the play segment may becontracted. This permits a reduced emphasis to be placed on thosesegments less likely to be exciting. It is to be understood that thelayered summarization may be based upon other factors, as desired.

Another module that may be included is a goal post detection module.Goal posts are normally pained yellow and have a predetermined U-shape.They normally appear in the image when there is a kick off and the endcameras are used to capture the video, as illustrated in FIG. 3. Thedetection of goal posts can be used to assist the detection of akick-off, especially when the camera has a very low shooting angle andthus making the field color based module less robust.

Another module that may be included within the system is a captiondetection module. Periodically football video includes captions on thelower or upper portion of the screen that contain information. Thesecaptions may be detected and analyzed to determine the occurrence of aparticular event, such as a home run. Further, the captions of thesummary segments may be analyzed to determine the type of event thatoccurred. In this manner, the summary segments may be furthercategorized for further refinement and hierarchical summarization.

From a typical hiking scene as illustrated in FIG. 2, it may be observedthat the top portion of the image is usually highly textured since itcorresponds to the audience area, while the lower portion is relativelysmooth. The present inventors determined that, in a hiking scene, theplayers' bodies usually result in textured regions. This textureinformation can be exploited to assist the detection of a hiking scene.The system may obtain a binary texture map as follows. For a pixel P0 inthe input frame, the system considers its neighbors, such as P₁˜P4, asillustrated in the following.

-   -   P1    -   P4 P0 P2    -   P3

Next, the system computes absolute luminance differences DYi=|Y0−Yi|,for i=1˜4, where Yi is the luminance value at pixel Pi. If more than two(2) out of the four (4) DY's are larger than a threshold, then P0 may beconsidered “textured”; otherwise, P0 is “non-textured”. The texture mapso-defined is not computationally expensive to obtain. Other texturecalculation techniques may likewise be used.

Referring to FIG. 27, the video summarization maybe included as part ofan MPEG-7 based browser/filter, where summarization is included withinthe standard. The media summarizer may be as shown in FIG. 1. Withdifferent levels of summarization built on top of the aforementionedvideo summarization technique, the system can provide the user withvarying levels of summaries according to their demands. Once the summaryinformation is described as an MPEG:-7 compliant XML document, one canutilize all the offerings of MPEG-7, such as personalization, wheredifferent levels of summaries can be offered to the user on the basis ofuser's preferences described in an MPEG-7 compliant way. Descriptions ofuser preferences in MPEG-7 include preference elements pertaining todifferent summary modes and detail levels.

In the case that the summarization is performed at a server or serviceprovider, the user downloads and receives the summary descriptionencoded in MPEG-7 format. Alternatively, in an interactive video ondemand (VOD) application, the media and its summary description resideat the provider's VOD server and the user (e.g., remote) consumes thesummary via a user-side browser interface. In this case, the summary maybe enriched further by additional information that may be added by theservice provider. Further, summarization may also be performed by theclient.

Referring to FIG. 28, the output of the module that automaticallydetects important segments may be a set of indices of segmentscontaining plays and important parts of the input video program. Adescription document, such as an MPEG-7 or TV-Anytime compliantdescription is generated in The Description Generation module. Summarysegments are made available to the Post-Processing module by TheExtraction of Summary Segments module which processes the input videoprogram according to the description. A post-processing module processesthe summary Segments and/or the description to generate the finalsummary video and final description. The post-processing module puts thepost-processed segments together to form the final summary video. Thepost-processing module may transcode the resulting video to a formatdifferent that of the input video to meet the requirements of thestorage/transmission channel. The final description may also be encoded,e.g., binarized if it is generated originally in textual format such asXML. Post-processing may include adding to the original audio track acommentary, insertion of advertisement segments, or metadata. Incontrast to play detection, post-processing may be completely, or inpart, manual processing. It may include, for example, automatic rankingand subset selection of events on the basis of automatic detection offeatures in the audio track associated with video segments. Thisprocessing may be performed at the server and then the resulting videotransferred to the client, normally over a network. Alternatively, theresulting video is included in a VOD library and made available to userson a VOD server.

Referring to FIG. 29, a system may be developed that incorporates startdetection of a play, end detection of a play, and summarization. Thedetection technique may be based upon processing a single frame,multiple frames, or a combination thereof.

The terms and expressions which have been employed in the foregoingspecification are used therein as terms of description and not oflimitation, and there is no intention, in the use of such terms andexpressions, of excluding equivalents of the features shown anddescribed or portions thereof, it being recognized that the scope of theinvention is defined and limited only by the claims which follow.

1. A method of processing a video including football played on a field,said method comprising: (a) identifying a plurality of segments of saidvideo, wherein the start of said plurality of segments is identifiedbased upon identifying a convergence location of a plurality of fieldlines that converge to generally the same location at a point outside ofsaid field, where each of said segments includes a plurality of framesof said video; and (b) creating a summarization of said video byincluding said plurality of segments, where said summarization includesfewer frames than said video.
 2. The method of claim 1, wherein saidlines are generally white.
 3. The method of claim 1, wherein said linesare parallel in the image domain.
 4. The method of claim 1, wherein saidlines are generally white and the background of said lines is generallygreen.
 5. The method of claim 1, wherein said generally same location isoutside of said frame of said video.
 6. The method of claim 1, whereinsaid lines are identified on the same frame of said video.